This I-Corps project focuses on the development of a digital transaction analysis system. The technology uses advanced algorithms to quickly analyze large amounts of fast-moving data. It creates real-time summaries of complex transaction data, making it possible to calculate detailed insights quickly and accurately, something that most current data analysis systems struggle to do on time. The key innovation is a set of algorithms that are proven to be accurate, fast, and efficient with memory. The algorithms are also designed to work well across multiple computers at once. This makes the approach better and faster than current systems, which often rely on basic averages or slow processing. The project provides a data analytics platform that leverages advanced algorithms to process petabyte-scale datasets and high-velocity data streams in real-time. By creating compact data summaries that retain essential information while dramatically reducing computational costs, the technology enables fraud detection systems to make accurate decisions within milliseconds while handling billions of transactions. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of the technology. This solution is based on the development of advanced algorithmic techniques that derive insights from high velocity data streams. Specifically, the technology employs mathematical constructs to create real-ti